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\begin{document}
% Page heads
\markboth{V. Catania et al.}{Parameter Space Representation of Pareto Front to
Explore Hardware-Software Dependencies}


\title{Parameter Space Representation of Pareto Front to
Explore Hardware-Software Dependencies}
\author{VINCENZO CATANIA
\affil{University of Catania}
ANDREA ARALDO
\affil{Universit\'e Paris-Sud and T\'el\'ecom ParisTech}
DAVIDE PATTI
\affil{University of Catania}
}

%\maketitle
\begin{abstract}
%Embedded Systems Design requires conflicting objectives to be
%optimized with an appropriate choice of hardware/software parameters.
%In this work we present PS, a new multi-objective exploration algorithm
%exploiting the parameter space representation of the pareto front.
%After a detailed formal description of the algorithm and the
%underlying concepts, we show a case study involving the
%hardware/software exploration of a VLIW architecture. Qualitative and
%quantitative comparisons of the PS approach against a well-known
%multi-objective genetic technique demonstrate that, while not
%outperforming it in terms of pareto dominance, the proposed approach
%can trade the uniformity and granularity qualities of the solutions
%found for obtaining larger Pareto sets, thus representing a further
%choice for designer when objective constrains allow it.


The Embedded Systems Design requires conflicting objectives to be
optimized with an appropriate choice of hardware/software parameters.
A simulation campaign can guide the design in finding the best
trade-offs, but, due to the big number of possible configurations, it
is often unfeasible to simulate them all. For these reasons, Design Space
Exploration (DSE) algorithms aim at finding near-optimal system
configurations, by simulating only a subset of them.

In this work we present PS, a new multi-objective optimization algorithm and evaluate it in the context of the embedded system design. The basic idea is to recognize \emph{interesting} regions, i.e.
regions of the configuration space that provide better
configurations with respect to other ones. PS evaluates more configurations in the
interesting regions, while less thoroughly exploring the rest of the
configuration space. After a detailed formal description of the
algorithm and the underlying concepts, we show a 
case study involving the hardware/software exploration of a VLIW
architecture. Qualitative and quantitative comparisons of PS
against a well-known multi-objective genetic approach demonstrate that,
while not outperforming it in terms of Pareto dominance, the proposed
approach can balance the uniformity and granularity qualities of the
solutions found, obtaining more extended Pareto-fronts that provide a wider view of the potentiality of the designed device. Therefore, PS represents a
further valid choice for the designer, when objective constrains allow it.
\end{abstract}

\category{D.2.2}{Design Tools and Techniques}{}

\terms{Design, Algorithms, Performance}

\keywords{DSE, MultiObjective Optimization, Genetic Algorithms}

\acmformat{Vincenzo Catania, Andrea Araldo and Davide Patti, 2014. Parameter Space Representation of Pareto Front to
Explore Hardware-Software Dependencies.}

\begin{bottomstuff}
Author's addresses: V. Catania, DIEEI University of Catania;
A. Araldo, Universit\'e Paris-Sud and T\'el\'ecom ParisTech;
D. Patti, DIEEI University of Catania.
\end{bottomstuff}

\maketitle
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\section{Conclusions}
\secL{Conclusions}
In this work we presented PS, a multi-objective strategy which 
introduces the concept of Parameter Space representation of Pareto
Front. A case study of a VLIW architecture involving strong
hardware/software dependencies has been analyzed to evaluate
the PS effectiveness with different amounts of simulations budget.
A qualitative/quantitative comparison against a widespread multi-objective genetic
approach showed that the proposed strategy can result in a different
distribution of Pareto points which balances solutions granularity and
improved Pareto extension. 

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